


from pathlib import Path

import numpy as np
from PIL import Image
def 							UpperCamelCase_(					_A :np.ndarray	)->		np.ndarray:
			UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
			return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def 							UpperCamelCase_(					_A :np.ndarray	)->		np.ndarray:
			return (gray > 1_27) & (gray <= 2_55)
def 							UpperCamelCase_(					_A :np.ndarray					,  _A :np.ndarray	)->		np.ndarray:
			UpperCamelCase__       =							np.zeros_like(_A	)
			UpperCamelCase__       =							np.zeros(
			    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

			# Copy image to padded image
			UpperCamelCase__       =							image

			# Iterate over image & apply kernel
			for x in range(image.shape[1]	):
						for y in range(image.shape[0]	):
									UpperCamelCase__       =							(
									    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
									).sum()
									UpperCamelCase__       =							int(summation > 0	)
			return output


if __name__ == "__main__":
						# read original image
						__UpperCamelCase     		=							Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
						__UpperCamelCase     		=							np.array(Image.open(lena_path))
						# kernel to be applied
						__UpperCamelCase     		=							np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
						__UpperCamelCase     		=							dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
						# Save the output image
						__UpperCamelCase     		=							Image.fromarray(output).convert('RGB')
						pil_img.save('result_dilation.png')






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		'trocr'
			_UpperCamelCase		:       Union[str, Any] 			=		['past_key_values']
			_UpperCamelCase		:       Union[str, Any] 			=		{
			    'num_attention_heads': 'decoder_attention_heads',
			    'hidden_size': 'd_model',
			    'num_hidden_layers': 'decoder_layers',
			}




			def __init__(				self   ,    snake_case=50265   ,    snake_case=1024   ,    snake_case=12   ,    snake_case=16   ,    snake_case=4096   ,    snake_case="gelu"   ,    snake_case=512   ,    snake_case=0.1   ,    snake_case=0.0   ,    snake_case=0.0   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=0.0   ,    snake_case=True   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							d_model
						UpperCamelCase__       =							decoder_layers
						UpperCamelCase__       =							decoder_attention_heads
						UpperCamelCase__       =							decoder_ffn_dim
						UpperCamelCase__       =							activation_function
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							dropout
						UpperCamelCase__       =							attention_dropout
						UpperCamelCase__       =							activation_dropout
						UpperCamelCase__       =							init_std
						UpperCamelCase__       =							decoder_layerdrop
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							scale_embedding
						UpperCamelCase__       =							use_learned_position_embeddings
						UpperCamelCase__       =							layernorm_embedding

						super().__init__(
						    pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    decoder_start_token_id=snake_case   ,    **snake_case   ,    )


